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Most of the state-of-the-art reinforcement learning algorithms are based on Bellman equations and make use of fixed-point iteration methods to converge to suboptimal solutions. However, some of the recent approaches transform the reinforcement learning problem into an equivalent likelihood maximization problem with using appropriate graphical models. Hence,(More)
Interaction with human musicians is a challenging task for robots as it involves online perception and precise synchronization. In this paper, we present a consistent and theoretically sound framework for combining perception and control for accurate musical timing. For the perception, we develop a hierarchical hidden Markov model that combines event(More)
In this study, a system with reinforcement learning for push-pull mesh based video streaming applications running over p2p networks is designed. In push-pull based video streaming systems, each node in the system may receive video data from more than one parent. In the proposed system, a node which started to receive insufficient video data from any parent(More)
Reinforcement learning problems are generally solved by using fixed-point iterations that converge to the suboptimal solutions of Bellman equations. However, it is also possible to formalize this problem as an equivalent likelihood maximization problem and employ probabilistic inference methods. We proposed an expectation-maximization algorithm that(More)
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